18366298. FREESPACE DETECTION USING MACHINE LEARNING FOR AUTONOMOUS SYSTEMS AND APPLICATIONS simplified abstract (NVIDIA Corporation)

FREESPACE DETECTION USING MACHINE LEARNING FOR AUTONOMOUS SYSTEMS AND APPLICATIONS

Organization Name

NVIDIA Corporation

Inventor(s)

Alexander Popov of Kirkland WA (US)

David Nister of Bellevue WA (US)

Nikolai Smolyanskiy of Seattle WA (US)

PATRIK Gebhardt of Cupertino CA (US)

Ke Chen of Mountain View CA (US)

Ryan Oldja of Issaquah WA (US)

Hee Seok Lee of Bundang-gu (KR)

Shane Murray of San Jose CA (US)

Ruchi Bhargava of Redmond WA (US)

Tilman Wekel of San Jose CA (US)

Sangmin Oh of San Jose CA (US)

FREESPACE DETECTION USING MACHINE LEARNING FOR AUTONOMOUS SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18366298 titled 'FREESPACE DETECTION USING MACHINE LEARNING FOR AUTONOMOUS SYSTEMS AND APPLICATIONS

Simplified Explanation

The patent application relates to freespace detection using machine learning models. Data from sensors is used to identify freespace within an operational environment, which is then annotated with freespace labels and combined with additional sensor data to create freespace annotated data for training machine learning models.

• Data from sensors is used to identify freespace within an operational environment
• Freespace labels are added to the data to annotate the freespace
• Additional sensor data is combined with the annotated data to create freespace annotated data
• Machine learning models are trained using the freespace annotated data to detect freespace

Potential Applications

This technology could be applied in autonomous vehicles, robotics, and surveillance systems to improve navigation and obstacle avoidance capabilities.

Problems Solved

This technology solves the problem of accurately detecting freespace within complex operational environments, which is crucial for safe and efficient navigation of autonomous systems.

Benefits

The benefits of this technology include improved accuracy in freespace detection, enhanced safety in navigation systems, and increased efficiency in autonomous operations.

Potential Commercial Applications

Potential commercial applications of this technology include autonomous vehicles, drones, warehouse robots, and security systems.

Possible Prior Art

One possible prior art for this technology could be the use of machine learning models for object detection and classification in various industries, such as computer vision and robotics.

What are the specific machine learning models used in this technology?

The specific machine learning models used in this technology are not mentioned in the abstract. It would be helpful to know which models are being utilized for freespace detection.

How is the accuracy of the freespace detection measured and validated?

The abstract does not provide information on how the accuracy of the freespace detection is measured and validated. Understanding the validation process would be important for assessing the reliability of the technology.

Original Abstract Submitted

Systems and methods are disclosed that relate to freespace detection using machine learning models. First data that may include object labels may be obtained from a first sensor and freespace may be identified using the first data and the object labels. The first data may be annotated to include freespace labels that correspond to freespace within an operational environment. Freespace annotated data may be generated by combining the one or more freespace labels with second data obtained from a second sensor, with the freespace annotated data corresponding to a viewable area in the operational environment. The viewable area may be determined by tracing one or more rays from the second sensor within the field of view of the second sensor relative to the first data. The freespace annotated data may be input into a machine learning model to train the machine learning model to detect freespace using the second data.